We admit it. Artificial Intelligence (AI) is overwhelming. It’s especially overwhelming for traditional industries such as publishing, which may not know how to get started with implementing AI in the publication process.
If your education organization is interested in adopting artificial intelligence in order to reduce production costs or increase accuracy of meta-tagged content, but you don’t know exactly how to get started, this blog post will give you an idea of what to expect. We’ve grouped these AI implementation steps into four broad categories:
- Understanding the purpose of AI
- Building a prototype solution
- Evaluation of the AI prototype
- Integrating the final product
In this post, we’ll touch on each of these areas so you’ll get a better understanding of how the implementation process brings you towards your AI goals.
Step 1: Understanding your goals for AI
Before your educational organization can implement an AI solution, it’s important to understand what you would like to achieve with the technology. That means you’ll need to take a dive into your operations and learn what AI can improve and what it can’t.
At EDIA, we present our capabilities and then analyze how it can solve a customer pain. We do this by facilitating workshops with different stakeholders across your organization. From content managers, authors, developers, to c-level executives, we listen to each stakeholders’ needs as a starting point for developing an AI solution. By working together with you to understand what the end goals of the AI are, we can build a better engine that meets your needs.
Additionally, these workshops also ensure that everybody within the organization has a clear understanding of what to expect from AI. This is critical for the successful adoption of the solution, as McKinsey noted in their discussion paper on AI, and leads to overall higher satisfaction with the technology.
The process of developing a prototype can take as little as 2 weeks but averages at 5 weeks. Often an already meta-tagged library is needed to train a custom AI model. If a standard AI (e.g. CEFR classifier) is to be used, this step can be skipped.
Our automated metadata tagging machine is explicitly designed for publishers who want to unlock smart content capabilities within their content management. Our prototypes are typically ready for testing within a matter of 3-4 weeks.
Step 3: Evaluation of the AI prototype
After building an AI prototype for your organization, it’s time to test the solution.
Testing before launching full implementation is useful for several reasons. Testing primarily helps developers understand how they can improve the solution. However, testing also primarily demonstrates the sheer power of AI to achieve your business goals.
At EDIA, conducting a test is relatively easy. We first take a small amount of content from your library that has previously been meta-tagged by your human staff. This portion of your content is wiped clean of its human-created metadata tags to recreate “raw” data. It’s then fed into the machine for automated metadata tagging, then the final output is compared to the original human-generated metadata tags.
The results of these tests can vary greatly, and numerous factors affect the outcome of the machine. However, the AI can often be as accurate as humans when it comes to metadata tagging. In many cases, it is even more accurate.
After testing, the developers work to fix any small bugs or make improvements that can lead to higher accuracy or efficiency overall. After making these changes, it’s time for the final step: full AI implementation.
Step 4: Integrating the AI solution
After listening to what your organizational goals are, then building, evaluation, and improving a solution, it’s time to implement it at a full scale across your organization. While you may have seen the power of the AI solution during the testing stage, fully integrating the AI into your operations will allow everyone in your organization to experience it first-hand.
Our clients report that AI improves their accuracy, increases their operational speed, reduces costs and boosts employee happiness. Employees no longer have to worry about tedious data entry for content management. Authors and content managers have better access to existing libraries through smart search and content curation features.
Publishers: Want your own roadmap to AI success?
We’ve chosen to focus specifically on educational publishing because of our experience in this industry. If you need more credibility from us, start by downloading our case study to learn how EDIA can help you build a roadmap for AI success.